"""simple docstring"""



import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


SCREAMING_SNAKE_CASE__:Any									=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:List[Any]									=			OrderedDict(
    [
        ("""audio-spectrogram-transformer""", """ASTFeatureExtractor"""),
        ("""beit""", """BeitFeatureExtractor"""),
        ("""chinese_clip""", """ChineseCLIPFeatureExtractor"""),
        ("""clap""", """ClapFeatureExtractor"""),
        ("""clip""", """CLIPFeatureExtractor"""),
        ("""clipseg""", """ViTFeatureExtractor"""),
        ("""conditional_detr""", """ConditionalDetrFeatureExtractor"""),
        ("""convnext""", """ConvNextFeatureExtractor"""),
        ("""cvt""", """ConvNextFeatureExtractor"""),
        ("""data2vec-audio""", """Wav2Vec2FeatureExtractor"""),
        ("""data2vec-vision""", """BeitFeatureExtractor"""),
        ("""deformable_detr""", """DeformableDetrFeatureExtractor"""),
        ("""deit""", """DeiTFeatureExtractor"""),
        ("""detr""", """DetrFeatureExtractor"""),
        ("""dinat""", """ViTFeatureExtractor"""),
        ("""donut-swin""", """DonutFeatureExtractor"""),
        ("""dpt""", """DPTFeatureExtractor"""),
        ("""encodec""", """EncodecFeatureExtractor"""),
        ("""flava""", """FlavaFeatureExtractor"""),
        ("""glpn""", """GLPNFeatureExtractor"""),
        ("""groupvit""", """CLIPFeatureExtractor"""),
        ("""hubert""", """Wav2Vec2FeatureExtractor"""),
        ("""imagegpt""", """ImageGPTFeatureExtractor"""),
        ("""layoutlmv2""", """LayoutLMv2FeatureExtractor"""),
        ("""layoutlmv3""", """LayoutLMv3FeatureExtractor"""),
        ("""levit""", """LevitFeatureExtractor"""),
        ("""maskformer""", """MaskFormerFeatureExtractor"""),
        ("""mctct""", """MCTCTFeatureExtractor"""),
        ("""mobilenet_v1""", """MobileNetV1FeatureExtractor"""),
        ("""mobilenet_v2""", """MobileNetV2FeatureExtractor"""),
        ("""mobilevit""", """MobileViTFeatureExtractor"""),
        ("""nat""", """ViTFeatureExtractor"""),
        ("""owlvit""", """OwlViTFeatureExtractor"""),
        ("""perceiver""", """PerceiverFeatureExtractor"""),
        ("""poolformer""", """PoolFormerFeatureExtractor"""),
        ("""regnet""", """ConvNextFeatureExtractor"""),
        ("""resnet""", """ConvNextFeatureExtractor"""),
        ("""segformer""", """SegformerFeatureExtractor"""),
        ("""sew""", """Wav2Vec2FeatureExtractor"""),
        ("""sew-d""", """Wav2Vec2FeatureExtractor"""),
        ("""speech_to_text""", """Speech2TextFeatureExtractor"""),
        ("""speecht5""", """SpeechT5FeatureExtractor"""),
        ("""swiftformer""", """ViTFeatureExtractor"""),
        ("""swin""", """ViTFeatureExtractor"""),
        ("""swinv2""", """ViTFeatureExtractor"""),
        ("""table-transformer""", """DetrFeatureExtractor"""),
        ("""timesformer""", """VideoMAEFeatureExtractor"""),
        ("""tvlt""", """TvltFeatureExtractor"""),
        ("""unispeech""", """Wav2Vec2FeatureExtractor"""),
        ("""unispeech-sat""", """Wav2Vec2FeatureExtractor"""),
        ("""van""", """ConvNextFeatureExtractor"""),
        ("""videomae""", """VideoMAEFeatureExtractor"""),
        ("""vilt""", """ViltFeatureExtractor"""),
        ("""vit""", """ViTFeatureExtractor"""),
        ("""vit_mae""", """ViTFeatureExtractor"""),
        ("""vit_msn""", """ViTFeatureExtractor"""),
        ("""wav2vec2""", """Wav2Vec2FeatureExtractor"""),
        ("""wav2vec2-conformer""", """Wav2Vec2FeatureExtractor"""),
        ("""wavlm""", """Wav2Vec2FeatureExtractor"""),
        ("""whisper""", """WhisperFeatureExtractor"""),
        ("""xclip""", """CLIPFeatureExtractor"""),
        ("""yolos""", """YolosFeatureExtractor"""),
    ]
)

SCREAMING_SNAKE_CASE__:Any									=			_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)



def 	_lowerCamelCase(	a				):
       for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
              if class_name in extractors:
                     __a       =    model_type_to_module_name(a				)

                     __a       =    importlib.import_module(F".{module_name}"   ,    "transformers.models"				)
                     try:
                            return getattr(a   ,    a				)
                     except AttributeError:
                            continue

       for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
              if getattr(a   ,    "__name__"   ,    a				) == class_name:
                     return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
       __a       =    importlib.import_module("transformers"				)
       if hasattr(a   ,    a				):
              return getattr(a   ,    a				)

       return None



def 	_lowerCamelCase(	a   ,    a = None   ,    a = False   ,    a = False   ,    a = None   ,    a = None   ,    a = None   ,    a = False   ,    **a   ,    ):
       __a       =    get_file_from_repo(
           a   ,    a   ,    cache_dir=a   ,    force_download=a   ,    resume_download=a   ,    proxies=a   ,    use_auth_token=a   ,    revision=a   ,    local_files_only=a   ,    )
       if resolved_config_file is None:
              logger.info(
                  "Could not locate the feature extractor configuration file, will try to use the model config instead."				)
              return {}

       with open(a   ,    encoding="utf-8"				) as reader:
              return json.load(a				)






class  snake_case__      :


 def __init__( self						):
        raise EnvironmentError(
            "AutoFeatureExtractor is designed to be instantiated "
            "using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method."						)


 @classmethod
 @replace_list_option_in_docstrings(lowerCamelCase						)
 def 						a__   ( cls    ,		lowerCamelCase    ,		**lowerCamelCase						):
        __a       =    kwargs.pop("config"    ,		lowerCamelCase						)
        __a       =    kwargs.pop("trust_remote_code"    ,		lowerCamelCase						)
        __a       =    True

        __a ,   __a       =    FeatureExtractionMixin.get_feature_extractor_dict(lowerCamelCase    ,		**lowerCamelCase						)
        __a       =    config_dict.get("feature_extractor_type"    ,		lowerCamelCase						)
        __a       =    None
        if "AutoFeatureExtractor" in config_dict.get("auto_map"    ,		{}						):
               __a       =    config_dict["auto_map"]["AutoFeatureExtractor"]

        # If we don't find the feature extractor class in the feature extractor config, let's try the model config.
        if feature_extractor_class is None and feature_extractor_auto_map is None:
               if not isinstance(lowerCamelCase    ,		lowerCamelCase						):
                      __a       =    AutoConfig.from_pretrained(lowerCamelCase    ,		**lowerCamelCase						)
               # It could be in `config.feature_extractor_type``
               __a       =    getattr(lowerCamelCase    ,		"feature_extractor_type"    ,		lowerCamelCase						)
               if hasattr(lowerCamelCase    ,		"auto_map"						) and "AutoFeatureExtractor" in config.auto_map:
                      __a       =    config.auto_map["AutoFeatureExtractor"]

        if feature_extractor_class is not None:
               __a       =    feature_extractor_class_from_name(lowerCamelCase						)

        __a       =    feature_extractor_auto_map is not None
        __a       =    feature_extractor_class is not None or type(lowerCamelCase						) in FEATURE_EXTRACTOR_MAPPING
        __a       =    resolve_trust_remote_code(
            lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						)

        if has_remote_code and trust_remote_code:
               __a       =    get_class_from_dynamic_module(
                   lowerCamelCase    ,		lowerCamelCase    ,		**lowerCamelCase						)
               __a       =    kwargs.pop("code_revision"    ,		lowerCamelCase						)
               if os.path.isdir(lowerCamelCase						):
                      feature_extractor_class.register_for_auto_class()
               return feature_extractor_class.from_dict(lowerCamelCase    ,		**lowerCamelCase						)
        elif feature_extractor_class is not None:
               return feature_extractor_class.from_dict(lowerCamelCase    ,		**lowerCamelCase						)
        # Last try: we use the FEATURE_EXTRACTOR_MAPPING.
        elif type(lowerCamelCase						) in FEATURE_EXTRACTOR_MAPPING:
               __a       =    FEATURE_EXTRACTOR_MAPPING[type(lowerCamelCase						)]
               return feature_extractor_class.from_dict(lowerCamelCase    ,		**lowerCamelCase						)

        raise ValueError(
            F"Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a "
            F"`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following "
            F"`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()						)}"						)






 @staticmethod
 def 						a__   ( lowerCamelCase    ,		lowerCamelCase						):
        FEATURE_EXTRACTOR_MAPPING.register(lowerCamelCase    ,		lowerCamelCase						)

"""simple docstring"""



def 	_lowerCamelCase(	a = 1   ,    a = 1_0_0_0				):
       __a       =    1
       __a       =    0

       for divide_by_number in range(a   ,    digit + 1				):
              __a       =    []
              __a       =    numerator
              for _ in range(1   ,    digit + 1				):
                     if now_divide in has_been_divided:
                            if longest_list_length < len(a				):
                                   __a       =    len(a				)
                                   __a       =    divide_by_number
                     else:
                            has_been_divided.append(a				)
                            __a       =    now_divide * 1_0 % divide_by_number

       return the_digit


# Tests
if __name__ == "__main__":
   import doctest

   doctest.testmod()
